An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated rec...An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.展开更多
电池荷电状态(state of charge,SOC)的准确估计近年来成为新能源发展的重中之重,也是电池管理系统(BMS)中最核心的部分。针对改进卡尔曼滤波算法(EKF)与门控循环单元神经网络算法(GRU)的缺陷,提出了一种基于3DGRU-EKF的改进SOC估算算法...电池荷电状态(state of charge,SOC)的准确估计近年来成为新能源发展的重中之重,也是电池管理系统(BMS)中最核心的部分。针对改进卡尔曼滤波算法(EKF)与门控循环单元神经网络算法(GRU)的缺陷,提出了一种基于3DGRU-EKF的改进SOC估算算法。首先使用二阶RC电池等效模型,利用复合脉冲功率特性测试(HPPC)进行电池参数辨识;随后对电池模型进行状态空间方程的建立,并利用EKF算法进行更新迭代来估算电池的SOC,可以得到卡尔曼增益与SOC估算误差;最后将2个量结合HPPC工况下的电压与电流作为3DGRU网络的输入,真实的SOC作为输出来训练神经网络。实验结果表明,提出的3DGRUEKF算法估算SOC的均方根误差(RMSE)与平均绝对误差(MAE)均小于0.5%,具有良好的效果。展开更多
基金funded by“The Pearl River Talent Recruitment Program”of Guangdong Province in 2019(Grant No.2019CX01G338)the Research Funding of Shantou University for New Faculty Member(Grant No.NTF19024-2019).
文摘An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.
文摘电池荷电状态(state of charge,SOC)的准确估计近年来成为新能源发展的重中之重,也是电池管理系统(BMS)中最核心的部分。针对改进卡尔曼滤波算法(EKF)与门控循环单元神经网络算法(GRU)的缺陷,提出了一种基于3DGRU-EKF的改进SOC估算算法。首先使用二阶RC电池等效模型,利用复合脉冲功率特性测试(HPPC)进行电池参数辨识;随后对电池模型进行状态空间方程的建立,并利用EKF算法进行更新迭代来估算电池的SOC,可以得到卡尔曼增益与SOC估算误差;最后将2个量结合HPPC工况下的电压与电流作为3DGRU网络的输入,真实的SOC作为输出来训练神经网络。实验结果表明,提出的3DGRUEKF算法估算SOC的均方根误差(RMSE)与平均绝对误差(MAE)均小于0.5%,具有良好的效果。
